Implementing Vector Search in Azure Cosmos DB for NoSQL

As AI applications demand more context-aware and semantic capabilities, traditional keyword search is no longer enough. Understanding how to store and query high-dimensional vector data is essential for modern developers building intelligent systems. This text-only course guides you through the fundamentals of vector databases and shows you exactly how to implement vector search within Azure Cosmos DB for NoSQL. You will transition from understanding basic database structures to executing complex semantic similarity queries that power modern AI features. What you'll learn: 1. Understand the core concepts of vector embeddings and semantic search. 2. Configure Azure Cosmos DB for NoSQL containers to store vector data. 3. Write queries to perform similarity searches using vector distance functions. 4. Integrate vector search with Retrieval-Augmented Generation (RAG) architectures. 5. Apply best practices for indexing and optimizing vector queries for performance. The course starts with foundational definitions and key terminology before walking you through schema design, index configuration, and query execution. You will progress from basic setups to practical integration patterns using clear, step-by-step written explanations and structured SQL examples. This course is designed for software developers, database enthusiasts, and aspiring AI engineers who are new to vector databases. No prior experience with vector search or machine learning is required, though a basic familiarity with SQL concepts is helpful. Start reading today to unlock the potential of semantic search in your database applications.